The CoMLSim has a testing error of \(0.019\)  averaged over 200 unseen testing samples as compared to a second-order finite difference solver. The CoMLSim error compares well with FNO [1]}, which has an error of \(0.021\) . Although the testing errors are very similar in this case, the main advantage of the CoMLSim approach is to accurately model local solution features on highly resolved grids and out-of-distribution generalizability as presented in the different use cases in the main paper as well as the supplementary materials.
